Serial Supply Chain Research Articles

Sharing planned factory shutdown information in supply chain systems: an agent-based modelling approach

Reverse information sharing (RIS) has recently received attention as an essential complementary coordination mechanism to the typical downstream IS in supply chain (SC) systems. This paper comprehensively discusses the behaviour of a SC system under the condition of planned factory shutdown. The significance of sharing shutdown information for the whole SC and each of its actors is investigated under different inventory/backorder cost ratios (I/B-CRs) and various production capacities. A modified beer game that allows factory shutdown is represented as a four-stage serial SC. A deterministic market demand, with demand IS in place, is assumed. An agent-based model is developed for simulating different scenarios. The results demonstrate that RIS prevents the accumulation of backorder and reduces the overall SC cost by almost 50%. The results, nevertheless, show that the significance of RIS is relative to the I/B-CR and production capacity.

Analysis of the bullwhip effect in two parallel supply chains with interacting price-sensitive demands

This paper offers insights into how the bullwhip effect in two parallel supply chains with interacting price-sensitive demands is affected in contrast to the situation of a single product in a serial supply chain. In particular, this research studies two parallel supply chains, each consisting of a manufacturer and a retailer, and the external demand for a single product depends on its price and the other's price in a situation in which each price follows a first-order autoregressive process. In this paper, we propose an analytical framework that incorporates two parallel supply chains, and we explore their interactions to determine the bullwhip effect. We identify the conditions under which the bullwhip effect is amplified or lessened with interacting price-sensitive demands relative to the situation without interaction.

Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate

The paper considers a closed-loop serial supply chain consisting of a raw material supplier, a manufacturer, a retailer and a collector who collects the used product from consumers. The retailer's demand is met up by both manufacturing and remanufacturing. The manufacturing process is assumed to be imperfect as it can produce some defectives which are reworked in the same cycle itself. The remanufacturing of used items solely depends on the quality level of collected items. Two mathematical models are developed. The first model considers a single manufacturing–remanufacturing cycle, while the second model considers multiple manufacturing and remanufacturing cycles. Both the models are solved using algorithms developed for sequential and global optimizations. Numerical studies show that (i) the acceptance quality level of returned items and the length of the replenishment cycle for the retailer are lower in case of sequential optimization than those in global optimization, (ii) integration among supply chain members results in less number of shipments from the manufacturer to the retailer, and (iii) the joint total profit is higher when the integrated approach is adopted. The percentage increase in joint total profit with the integrated policy is 1.24% in the first model while it is 0.544% in the second model.

Decision support system for vendor managed inventory supply chain: a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).

Economic Lot Size Formula Under VMI Program with Poisson Demand

Nowadays, inventory management plays a crucial role in manufacturing supply chains. Vendor-managed inventory (VMI) program is a collaborative initiative that shows considerable performances in managing inventories through the supply system. In this study, we discuss the VMI initiative in a serial manufacturing supply chain when there is an uncertainty in demand and provide an approximate closed-form formula for the economic lot size. In this paper, we consider a two-echelon supply chain system consisting of a manufacturer and a retailer. Under vendor-managed inventory program with centralized decision making, the manufacturer is authorized to manage inventories of all agreed upon stock keeping units at the retailer’s location. We develop a renewal reward model for the case of Poisson demand and drive the mathematical formula of the long-run average total inventory cost of supply system under VMI program, and then, we develop a closed-form approximate for the optimal solution. Finally, a numerical experiment is run and measured to evaluate the performance of our approximation.

Supply Chain Performance Evaluation Using Spreadsheet Simulation

The objective of this paper is to develop a spreadsheet-based simulation methodology to evaluate the performance of a serial supply chain under different inventory position based ((r, Q) and (r, S)) and non-inventory position based (Fixed Order Quantity (FOQ) and Demand Flow Strategy (DFS)) periodic review inventory policies. The performance measures comprises of Fill Rate, Risk of Shortage, Bullwhip Effect and Total Cost of Supply Chain. An experimental study with four scenarios are carried out for two customer demand distributions under information sharing and no information sharing scenarios to determine performance measures in lost sales environment. The best performing inventory control policy for each scenario is found by conducting Grey Relational Analysis and the analysis show that one of non-inventory position based policy, FOQ is the better policy compared to other three policies.

Sequencing Appointments for Service Systems Using Inventory Approximations

Managing appointments for service systems with random job durations is a challenging task. We consider a class of appointment planning problems that involve two sets of decisions: job sequencing, i.e., determining the order in which a list of jobs should be performed by the server, and appointment scheduling, i.e., planning the starting times for jobs. These decisions are interconnected because their joint goal is to minimize the expected server idle time and job late-start penalty costs incurred because of randomness in job durations. In this paper, we design new heuristics for sequencing appointments. The idea behind the development of these heuristics is the structural connection between such appointment scheduling problems and stochastic inventory control in serial supply chains. In particular, the decision of determining time allowances as buffers against random job durations is analogous to that of selecting inventory levels as buffers to accommodate random demand in a supply chain; having excess buffers in appointment scheduling and supply chain settings incurs idle time and excess inventory holding costs, respectively, and having inadequate buffers leads to delays of subsequent jobs and backorders, respectively. Recognizing this connection, we propose tractable approximations for the job sequencing problem, obtain several insights, and further develop a very simple sequencing rule of ordering jobs by duration variance to late-start penalty cost ratio. Computational results show that our proposed heuristics produce close-to-optimal job sequences with significantly reduced computation times compared with those produced using an exact mixed-integer stochastic programming formulation based on the sample-average approximation approach.

Distribution and Equitable Sharing of Value From Information Sharing Within Serial Supply Chains

This paper studies the incentive issues that arise when firms in a multilevel supply chain create value jointly by investing in information sharing. We consider three types of information sharing: (1) supply-chain-wide information sharing; (2) downstream information sharing; and (3) upstream information sharing. We showed that the value of information sharing is higher for the upstream firms than for downstream firms regardless of information sharing type. Furthermore, the value of information sharing for any firm is higher under downstream information sharing than upstream information sharing, and the incremental value of information sharing to a firm decreases when more downstream firms share information. Therefore, if there is a cost associated with information sharing, then upstream firms have an incentive to free ride on downstream firms' information sharing efforts. These results suggest that serious incentive misalignments may impede supply-chain-wide information sharing, even though it maximizes the value to the supply chain, and that a mechanism to distribute the overall surplus equitably may become essential. If a contract distributes the surplus according to each firm's incremental contribution to it, then firms that are in the middle levels of the supply chain receive a higher share than those that are in either end of the supply chain. That is, interestingly, neither the firm that possesses the information that is propagated throughout the supply chain by information sharing nor the most upstream firm realizes the highest value from information sharing obtains the maximum share of the surplus generated under such a contract.

Vertical integration with endogenous contract leadership: Stability and fair profit allocation

This paper studies vertical integration in serial supply chains with a wholesale price contract. We consider a business environment where the contracting leader may be endogenously changed before and after forming the integration. A cooperative game is formulated to normatively analyze the stable and fair profit allocations under the grand coalition in such an environment. Our main result demonstrates that vertical integration is stable when all members are pessimistic in the sense that they are sure that they will not become the contracting leader if they deviate from the grand coalition. We find that in this case, the grand coalition’s profit must be allocated more to the retailer and the members with higher costs. Nevertheless, we also show the conditions under which the upstream manufacturer can have strong power as in traditional supply chains.

Optimal echelon stock policies for multi-stage supply chains in fuzzy environments

This paper investigates decision-making in multi-echelon serial supply chain management in the presence of imprecision or uncertainty arising from human reasoning, emphasising the computational resolution. The proposed analysis method is based on a combination of the extension principle and the alpha-representation in fuzzy theory and optimisation theory. The problem is first formulated as a fuzzy optimisation model with several fuzzy parameters. To conserve the fuzziness of the input information of the supply chain, such as forecast market demands and inventory costs, a pair of two-level mathematical programs is proposed to identify the lower and upper bounds of the fuzzy performance at different possibility levels, so that the complete membership function can be described. Four example scenarios are solved to demonstrate the validity of the proposed analysis method. The proposed methodology is widely applicable with different types of membership functions for fuzzy parameters, positive lead times or other more complicated cases. The managerial implications are also discussed for reference by decision-makers.

Evaluation of cycle-count policies for supply chains with inventory inaccuracy and implications on RFID investments

Inventory record inaccuracy leads to ineffective replenishment decisions and deteriorates supply chain performance. Conducting cycle counts (i.e., periodic inventory auditing) is a common approach to correcting inventory records. It is not clear, however, how inaccuracy at different locations affects supply chain performance and how an effective cycle-count program for a multi-stage supply chain should be designed. This paper aims to answer these questions by considering a serial supply chain that has inventory record inaccuracy and operates under local base-stock policies. A random error, representing a stock loss, such as shrinkage or spoilage, reduces the physical inventory at each location in each period. The errors are cumulative and are not observed until a location performs a cycle count. We provide a simple recursion to evaluate the system cost and propose a heuristic to obtain effective base-stock levels. For a two-stage system with identical error distributions and counting costs, we prove that it is more effective to conduct more frequent cycle counts at the downstream stage. In a numerical study for more general systems, we find that location (proximity to the customer), error rates, and counting costs are primary factors that determine which stages should get a higher priority when allocating cycle counts. However, it is in general not effective to allocate all cycle counts to the priority stages only. One should balance cycle counts between priority stages and non-priority stages by considering secondary factors such as lead times, holding costs, and the supply chain length. In particular, more cycle counts should be allocated to a stage when the ratio of its lead time to the total system lead time is small and the ratio of its holding cost to the total system holding cost is large. In addition, more cycle counts should be allocated to downstream stages when the number of stages in the supply chain is large. The analysis and insights generated from our study can be used to design guidelines or scorecard systems that help managers design better cycle-count policies. Finally, we discuss implications of our study on RFID investments in a supply chain.

Order Stability in Supply Chains: Coordination Risk and the Role of Coordination Stock

The bullwhip effect describes the tendency for the variance of orders in supply chains to increase as one moves upstream from consumer demand. We report on a set of laboratory experiments with a serial supply chain that tests behavioral causes of this phenomenon, in particular the possible influence of coordination risk. Coordination risk exists when individuals' decisions contribute to a collective outcome and the decision rules followed by each individual are not known with certainty, for example, where managers cannot be sure how their supply chain partners will behave. We conjecture that the existence of coordination risk may contribute to bullwhip behavior. We test this conjecture by controlling for environmental factors that lead to coordination risk and find these controls lead to a significant reduction in order oscillations and amplification. Next, we investigate a managerial intervention to reduce the bullwhip effect, inspired by our conjecture that coordination risk contributes to bullwhip behavior. Although the intervention, holding additional on‐hand inventory, does not change the existence of coordination risk, it reduces order oscillation and amplification by providing a buffer against the endogenous risk of coordination failure. We conclude that the magnitude of the bullwhip can be mitigated, but that its behavioral causes appear robust.

A recovery mechanism for a two echelon supply chain system under supply disruption

Supply chains are becoming increasingly competitive in order to meet customer demands. The task of optimizing highly evolved supply chains is not easy, especially when it is particularly sensitive to unexpected disruption. This paper presents a newly developed real-time recovery mechanism for a two stage serial supply chain system, consisting of one manufacturer and one retailer, where the production is disrupted for a given period of time during the production up time. The model is capable of determining the recovery schedule for the manufacturer and the retailer, and ensuring that the total relevant costs are minimized, while seeking to recover the original schedule by the end of the recovery time window. The model was solved using an efficient heuristic developed in this paper, which performed well in giving quality solutions within reasonable time. It can be shown that the optimal recovery schedule is dependent on the shortage cost parameters, as well as on the extent of the disruption. The presented model is useful to assist decision makers to take a pro-active approach for maintaining business continuity in the event of a disruption in the supply chain system.

Serial Inventory Systems with Markov-Modulated Demand: Solution Bounds, Asymptotic Analysis, and Insights

We study inventory control of serial supply chains with continuous, Markov-modulated demand (MMD). Our goal is to simplify the computational complexity by resorting to certain approximation techniques, and, in doing so, to gain a deeper understanding of the problem. First, we perform a derivative analysis of the problem's optimality equations, and develop general, analytical solution bounds for the optimal policy. This leads to simple-to-compute near-optimal heuristic solutions, which also reveal an intuitive relationship with the primitive model parameters. Second, we establish an MMD central limit theorem under long replenishment lead time through asymptotic analysis. We show that the relative errors between our heuristic and the optimal solutions converge to zero as the lead time becomes sufficiently long, with the rate of convergence being the square root of the lead time. Third, we show that, by leveraging the Laplace transform, the computational complexity of our heuristic is superior to the existing methods. Finally, we provide the first set of numerical study for serial systems under MMD. The numerical results demonstrate that our heuristic achieves near-optimal performance even under short lead times and outperforms alternative heuristics in the literature. In addition, we observe that, in an optimally run supply chain under MMD, the internal fill rate can be high and the demand variability propagating upstream can be dampened, both different from the system behaviors under stationary demand.

Bullwhip effect analysis using supply chain role play game and ranking of supply chains

The tendency of orders to increase in variability as one moves up a supply chain is commonly known as the bullwhip effect. In a supply chain, it creates a negative impact on the performance. The objective of the present study is to know the effect of change in customer demand magnitude on bullwhip effect in a four-stage serial supply chain and rank the supply chains using multi-criteria decision making. We conducted the supply chain role play game under no information sharing among the supply chain members and the retailer faces different customer demand magnitudes under backorder, lost sales environment. The statistical test results show that there is a significant difference between variance of orders when customer demand magnitude changes and bullwhip effect increases when demand magnitude increases. The supply chains are ranked based on inventory cost, fill rate and bullwhip effect. Grey relational analysis is used for ranking.

Analysis of Two-Commodity Continuous Review Inventory System in Supply Chain

Abstract: This paper considers a two commodity continuous review inventory system. Continuous review inventory control of a single item at a single location had been considered by many researchers in past. We extend this inventory control strategy to two-echelon system, which is a building block for serial supply chain. The inventory control system consists of two warehouse (WHi), Two Distribution Centre’s (DCi) each associated with a retailer and handling two non-identical products. A (s, S) type inventory system with Poisson demand and exponentially distributed lead times is assumed at retailer node. The items are supplied to the retailers in packs of Qi (= Si-si) items from the distribution center (DCi) which has instantaneous replenishment facility from an abundant source (manufacturer). The steady state probability distribution and the operating characteristics are obtained explicitly. The measures of system performance in the steady state are obtained. The results are illustrated with numerical examples.

Solving a tri-objective supply chain problem with modified NSGA-II algorithm

In this paper, we propose the modification of an existing Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm has been applied on a tri-objective problem for a two echelon serial supply chain. The objectives considered are: (1) minimization of the total cost of a two-echelon serial supply chain and (2) minimization of the variance of order quantity and (3) minimization of the total inventory. The variance of order quantity is an important factor to consider since the variance of order quantity is used to measure the bullwhip effect which is one of the performance measures of a supply chain. The supply chain under consideration is assumed to consist of buyers and supplier. The production process at the supplier is an imperfect production process and thus produces defective items. A percentage of defective items are sold at a secondary market and the remaining defective items are repaired. We have introduced a mutation algorithm which has been embedded in the proposed algorithm. Since the proposed mutation algorithm is performed over the entire population, thus the mutation algorithm has caused the modification of the parts of the original NSGA-II. The results of the modified algorithm have been compared with those of the original NSGA-II and SPEA2 (Strength Pareto Evolutionary Algorithm 2) evolutionary algorithms for varying values of probability of crossover. The experimental results show that the proposed algorithm performs significantly better than the original NSGA-II and SPEA2.

The incremental value of central control in serial supply chains

We consider a two-echelon serial supply chain where a single manufacturer replenishes a single downstream customer who faces random, stationary and discrete demand. In this setting, we compare the performance of a traditional supply chain having no information sharing to one where the customer shares demand and inventory information with the manufacturer. We also consider the case of central control where the manufacturer has full control over replenishments. This full information sharing with control by the manufacturer captures what has been called a vendor-managed inventory scenario in the literature. In order to estimate the performance of these three supply chain scenarios, we utilise renewal theory to develop probability models for each. A computational analysis of the models determines the benefit which information sharing offers as well as the incremental benefit central control provides beyond that of information sharing alone, a value which has not typically been considered in the literature despite its importance. Results indicate central control offers a 4.0% improvement over the no-information sharing setting; however, information sharing alone accounts for some of that benefit, offering an average cost savings of 1.8% compared to the no-information sharing setting. We then conclude that central control offers only an additional 2.2% benefit over the information sharing setting.

Sequencing Appointments for Service Systems Using Inventory Approximations

Managing appointments for service systems with random job durations is a challenging task. We consider a class of appointment planning problems that involve two sets of decisions: job sequencing, i.e., determining the order in which a list of jobs should be performed by the server, and appointment scheduling, i.e., planning the starting times for jobs. These decisions are interconnected because their joint goal is to minimize the expected server idle time and job late-start penalty costs incurred due to randomness in job durations. In this paper, we design new heuristics for sequencing appointments. The idea behind the development of these heuristics is the structural connection between such appointment scheduling problems and stochastic inventory control in serial supply chains. In particular, the decision of determining time allowances as buffers against random job durations is analogous to that of selecting inventory levels as buffers to accommodate random demand in a supply chain; having excess buffers in appointment scheduling and supply chain settings incurs idle time and excess inventory holding costs, respectively, and having inadequate buffers leads to delays of subsequent jobs and back orders, respectively. Recognizing this connection, we propose tractable approximations for the job sequencing problem, obtain several insights, and further develop a very simple sequencing rule of ordering jobs by duration variance to late-start penalty cost ratio. Computational results show that our proposed heuristics produce close-to-optimal job sequences with significantly-reduced computation times compared with those produced using an exact mixed integer stochastic programming formulation based on the sample-average approximation approach.The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2352901

Comparison of (s, S) and (R, T) Policies in a Serial Supply Chain with Information Sharing

It has been studied that retailer's using a suboptimal (R, T) policy is often more desirable to make the best use of information flows than the locally optimal (s, S) policy in a two-stage serial supply chain. In this paper, by performing an extensive computational study, we tabulate the benefit of the retailer's using (R, T) policy instead of (s, S) policy in a supply chain with information sharing, and compare it to a maximum possible benefit that could be achieved in a centralized supply chain. We can understand the mechanisms of how the cost parameters and demand variance affect the benefit of the retailer's using (R, T) policy instead of (s, S) policy, by comparing decentralized and centralized systems.